No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Adaptive IoT System for Precision Agriculture
... Lately, many fields, including agriculture, have seen an evolution in machine learning (ML) [13][14][15][16][17][18][19]. The techniques used in Machine learning are very convenient in finding crop demand because of their capability to go over giant complicated datasets, allow us to predict and expect what is coming, and give us precise outcomes [3]. ...
... For the design of the Machine Learning Modules of the evaluation cases, we evaluated different machine learning algorithms and models. The selection of the algorithms and models is based on their common use in the community; for some recent examples see [17,26,1]. In addition, the algorithms and models are supported by the widely used scikit-learn implementation kit [49] (see for instance [38,61,19]) that we also used for implementing the Machine Learning Module. ...
Modern software systems often have to cope with uncertain operation conditions, such as changing workloads or fluctuating interference in a wireless network. To ensure that these systems meet their goals these uncertainties have to be mitigated. One approach to realize this is self-adaptation that equips a system with a feedback loop. The feedback loop implements four core functions -- monitor, analyze, plan, and execute -- that share knowledge in the form of runtime models. For systems with a large number of adaptation options, i.e., large adaptation spaces, deciding which option to select for adaptation may be time consuming or even infeasible within the available time window to make an adaptation decision. This is particularly the case when rigorous analysis techniques are used to select adaptation options, such as formal verification at runtime, which is widely adopted. One technique to deal with the analysis of a large number of adaptation options is reducing the adaptation space using machine learning. State of the art has showed the effectiveness of this technique, yet, a systematic solution that is able to handle different types of goals is lacking. In this paper, we present ML2ASR+, short for Machine Learning to Adaptation Space Reduction Plus. Central to ML2ASR+ is a configurable machine learning pipeline that supports effective analysis of large adaptation spaces for threshold, optimization, and setpoint goals. We evaluate ML2ASR+ for two applications with different sizes of adaptation spaces: an Internet-of-Things application and a service-based system. The results demonstrate that ML2ASR+ can be applied to deal with different types of goals and is able to reduce the adaptation space and hence the time to make adaptation decisions with over 90%, with negligible effect on the realization of the adaptation goals.
In today's world, technology plays an integral role in addressing everyday challenges. Using technology in agriculture allows farmers to increase productivity while managing natural resources. Agriculture has always been plagued by pests, which destroy parts of the crops or even the whole field. Pest control at an early stage of crop development is a challenge for farmers. It eventually has a negative impact on the economy. By examining the odour substance of pests, a novel way to analyze pests is presented in this study. There is a distinct smell associated with every pest. Since odour can identify concealed pests, including deeply buried pests, swirled pests, etc., it is easier to detect than other detection methodologies. In light of this, based on odour as a key consideration, the proposed system evaluates five different odours, such as pungent, musty, misty, and sweet. In this case, gas sensors are analyzed, and then features are extracted using a Faster R-CNN algorithm. Additionally, it can be used to determine the density of infestations and counting. It is possible to develop process accuracy and timeliness using pseudocode. Comparatively, accuracy increased to 6% from Faster R CNN-based pest detection. Samples of the proposed development have been tested for performance metrics.
Modern software systems often have to cope with uncertain operation conditions, such as changing workloads or fluctuating interference in a wireless network. To ensure that these systems meet their goals these uncertainties have to be mitigated. One approach to realize this is self-adaptation that equips a system with a feedback loop. The feedback loop implements four core functions – monitor, analyze, plan, and execute – that share knowledge in the form of runtime models. For systems with a large number of adaptation options, i.e., large adaptation spaces, deciding which option to select for adaptation may be time consuming or even infeasible within the available time window to make an adaptation decision. This is particularly the case when rigorous analysis techniques are used to select adaptation options, such as formal verification at runtime, which is widely adopted. One technique to deal with the analysis of a large number of adaptation options is reducing the adaptation space using machine learning. State of the art has showed the effectiveness of this technique, yet, a systematic solution that is able to handle different types of goals is lacking. In this paper, we present ML2ASR+, short for Machine Learning to Adaptation Space Reduction Plus. Central to ML2ASR+ is a configurable machine learning pipeline that supports effective analysis of large adaptation spaces for threshold, optimization, and setpoint goals. We evaluate ML2ASR+ for two applications with different sizes of adaptation spaces: an Internet-of-Things application and a service-based system. The results demonstrate that ML2ASR+ can be applied to deal with different types of goals and is able to reduce the adaptation space and hence the time to make adaptation decisions with over 90%, with negligible effect on the realization of the adaptation goals.
IOT is a revolutionary technology that represents the future of computing and communications. It refers to a network of objects and is often a self-configurating wireless network. The single digital market, future internet, sharing of knowledge, social networks, protection of data and open access to wisdom will all be essential for securing global food security. The development of wisdom based systems for the farming sector has to be focused on Internet of Things including geomatics or 3S (RS, GIS and GPS), sensor technology, WSN, RFID and Cloud Computing. The increased use of geomatics in agriculture is adding to a greener agriculture and greater environmental stewardship while maintaining the economic viability of farming enterprises. Satellite and aerial imagery play a significant role in modern agriculture. Advances in image sensors and Wireless Sensor Networks (WSN) help to identify and delineate landscape to manageable field level food production zones more quickly and effectively than before and at much higher resolutions. Image processing software support these sensors providing greater analytical capabilities and improved knowledge than was previously possible. RFID technology is fairly mature and food traceability is now more common in the developed world. Agri-business organizations are increasingly becoming active in the social media. At least 43 per cent of the input suppliers to farm sector are active in social media such as LinkedIn, Twitter and You Tube which are the most popular platforms for agri-business companies. Cloud computing is "a new style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet". Despite the advances made in the technologies, application of IOT for agriculture still remains a formidable task, since integration of diverse domains for online monitoring of agricultural supply chain and management of complex agro ecosystems require concerted and collaborative efforts in a structured manner.
IOT is a revolutionary technology that represents the future of computing and communications. It refers to a network of objects and is often a self-configurating wireless network. The single digital market, future internet, sharing of knowledge, social networks, protection of data and open access to wisdom will all be essential for securing global food security. The development of wisdom based systems for the farming sector has to be focused on Internet of Things including geomatics or 3S (RS, GIS and GPS), sensor technology, WSN, RFID and Cloud Computing. The increased use of geomatics in agriculture is adding to a greener agriculture and greater environmental stewardship while maintaining the economic viability of farming enterprises. Satellite and aerial imagery play a significant role in modern agriculture. Advances in image sensors and Wireless Sensor Networks (WSN) help to identify and delineate landscape to manageable field level food production zones more quickly and effectively than before and at much higher resolutions. Image processing software support these sensors providing greater analytical capabilities and improved knowledge than was previously possible. RFID technology is fairly mature and food traceability is now more common in the developed world. Agri-business organizations are increasingly becoming active in the social media. At least 43 per cent of the input suppliers to farm sector are active in social media such as LinkedIn, Twitter and You Tube which are the most popular platforms for agri-business companies. Cloud computing is “a new style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet”. Despite the advances made in the technologies, application of IOT for agriculture still remains a formidable task, since integration of diverse domains for online monitoring of agricultural supply chain and management of complex agro ecosystems require concerted and collaborative efforts in a structured manner.
Architecting IoT systems able to guarantee Quality of Service (QoS) levels can be a challenging task due to the inherent uncertainties (induced by changes in e.g., energy availability, network traffic) that they are subject to. Existing work has shown that machine learning (ML) techniques can be effectively used at run time for selecting self-adaptation patterns that can help maintain adequate QoS levels. However, this class of approach suffers from learning bias, which induces accuracy problems that might lead to sub-optimal (or even unfeasible) adaptations in some situations. To overcome this limitation, we propose an approach for proactive self-adaptation which combines ML and formal quantitative verification (probabilistic model checking). In our approach, ML is tasked with selecting the best adaptation pattern for a given scenario, and quantitative verification checks the feasibility of the adaptation decision, preventing the execution of unfeasible adaptations and providing feedback to the ML engine which helps to achieve faster convergence towards optimal decisions. The results of our evaluation show that our approach is able to produce better decisions than ML and quantitative verification used in isolation.
Water management is paramount in countries with water scarcity. This also affects agriculture, as a large amount of water is dedicated to that use. The possible consequences of global warming lead to the consideration of creating water adaptation measures to ensure the availability of water for food production and consumption. Thus, studies aimed at saving water usage in the irrigation process have increased over the years. Typical commercial sensors for agriculture irrigation systems are very expensive, making it impossible for smaller farmers to implement this type of system. However, manufacturers are currently offering low-cost sensors that can be connected to nodes to implement affordable systems for irrigation management and agriculture monitoring. Due to the recent advances in IoT and WSN technologies that can be applied in the development of these systems, we present a survey aimed at summarizing the current state of the art regarding smart irrigation systems. We determine the parameters that are monitored in irrigation systems regarding water quantity and quality, soil characteristics and weather conditions. We provide an overview of the most utilized nodes and wireless technologies. Lastly, we will discuss the challenges and the best practices for the implementation of sensor-based irrigation systems.
Smart agriculture is a new concern in IoT systems. IoT technologies can provide information about the agriculture field and its surroundings and act accordingly. Architecting smart agriculture systems can benefit from architecting methods available for IoT systems. In this paper, we introduce CSA-CAPS, a Climate Smart Agriculture modeling framework designed based on CAPS. CAPS is an architecture-driven modeling framework for developing IoT Systems. CSA-CAPS includes a modified meta model for the software architecture of CAPS. It adds special sensors and functionalities for climate concerns of building smart agriculture systems. This paper shows the capability of CSA-CAPS by applying motivating scenarios.
Increasing food consumption, asking for quality food, and environmental impacts of agriculture lead to has used information technology in the agriculture sector, which comes under the heading of precision agriculture. Internet of things (IOT) is a technology that is growing rapidly in recent years and brings numerous benefits with it for agriculture. Because of the heterogeneous and enormous amount of data collected by IoT devices, future of internet of things (IoT) agricultural applications depend on cloud computing. At the same time, microcontrollers will add new abilities to the internet of things (IoT). In this survey, the research trend, the concepts, fundamental components of IOT, the challenges, and IOT applications in agriculture are examined. Firstly, the numbers of published papers in this field reviewed. Secondly, IOT definition and IOT architecture together with its layers are introduced. Thirdly, some involved technologies in the IOT are compared; finally, the main challenges in IOT and precision agriculture (PA) are considered.
Despite the perception people may have regarding the agricultural process, the reality is that today’s agriculture industry is data-centered, precise, and smarter than ever. The rapid emergence of the Internet-of-Things (IoT) based technologies redesigned almost every industry including “smart agriculture” which moved the industry from statistical to quantitative approaches. Such revolutionary changes are shaking the existing agriculture methods and creating new opportunities along a range of challenges. This article highlights the potential of wireless sensors and IoT in agriculture, as well as the challenges expected to be faced when integrating this technology with the traditional farming practices. IoT devices and communication techniques associated with wireless sensors encountered in agriculture applications are analyzed in detail. What sensors are available for specific agriculture application, like soil preparation, crop status, irrigation, insect and pest detection are listed. How this technology helping the growers throughout the crop stages, from sowing until harvesting, packing and transportation is explained. Furthermore, the use of unmanned aerial vehicles for crop surveillance and other favorable applications such as optimizing crop yield is considered in this article. State-of-the-art IoT-based architectures and platforms used in agriculture are also highlighted wherever suitable. Finally, based on this thorough review, we identify current and future trends of IoT in agriculture and highlight potential research challenges.
Self-adaptation is nowadays considered to be the best solution to dynamically reconfigure a system in the occurrence of deviations from the expected quality of service (QoS) parameters. However, data-and event-driven systems, such as IoT applications, impose new heterogeneity, interoperability, and distribution issues, that make uncertainty on the QoS stability even harder. Typical adaption techniques make use of reactive approaches, an after-the-fact adaptation that starts when the system deviates from the expected QoS parameters. What we envision is instead a proactive approach to anticipate the changes before the event of a QoS deviation. More specifically, we propose IoTArchML, a machine learning-driven approach for decision making in aiding proactive architectural adaptation of IoT system. The approach i) continuously monitors the QoS parameters; ii) predicts, based on historical data, possible deviations from the acceptable QoS parameters; iii) considers a list of possible alternative solutions to prevent the QoS deviation; iv) selects the optimal solution from the list; and v) checks whether the envisioned solution satisfies the overall system QoS requirements.
We, therefore, move the focus from self-adaptive architectures to self-learning architectures, enabling the architectures to learn and improve over a period of time.
This paper proposes AgriTalk, an inexpensive IoT platform for precision farming of soil cultivation. We conduct experiments on turmeric cultivation, which indicates that the turmeric quality is significantly enhanced through AgriTalk. Specifically, the curcumin concentration is up to 4500-5500 mg/100g, which is 5 times more than existing products. We demonstrate how to intuitively configure the connections between the sensors and the actuators with the desired farming intelligence, and to effectively maintain AgriTalk for precision farming. We conduct measurement, analytic analysis, and simulation experiments to investigate the IoT message delays of AgriTalk. Our study indicates that the delays for automatic control and automatic-manual control switching with long distances (more than 30 Km) are very short (less than 0.2 seconds) and AgriTalk can easily respond to quick and dynamic change of the field environment conditions in soil cultivation.
The smart management of freshwater for precision irrigation in agriculture is essential for increasing crop yield and decreasing costs, while contributing to environmental sustainability. The intense use of technologies offers a means for providing the exact amount of water needed by plants. The Internet of Things (IoT) is the natural choice for smart water management applications, even though the integration of different technologies required for making it work seamlessly in practice is still not fully accomplished. The SWAMP project develops an IoT-based smart water management platform for precision irrigation in agriculture with a hands-on approach based on four pilots in Brazil and Europe. This paper presents the SWAMP architecture, platform, and system deployments that highlight the replicability of the platform, and, as scalability is a major concern for IoT applications, it includes a performance analysis of FIWARE components used in the Platform. Results show that it is able to provide adequate performance for the SWAMP pilots, but requires specially designed configurations and the re-engineering of some components to provide higher scalability using less computational resources.
Rapid and accurate counting and recognition of flying insects are of great importance, especially for pest control. Traditional manual identification and counting of flying insects is labor intensive and inefficient. In this study, a vision-based counting and classification system for flying insects is designed and implemented. The system is constructed as follows: firstly, a yellow sticky trap is installed in the surveillance area to trap flying insects and a camera is set up to collect real-time images. Then the detection and coarse counting method based on You Only Look Once (YOLO) object detection, the classification method and fine counting based on Support Vector Machines (SVM) using global features are designed. Finally, the insect counting and recognition system is implemented on Raspberry PI. Six species of flying insects including bee, fly, mosquito, moth, chafer and fruit fly are selected to assess the effectiveness of the system. Compared with the conventional methods, the test results show promising performance. The average counting accuracy is 92.50% and average classifying accuracy is 90.18% on Raspberry PI. The proposed system is easy-to-use and provides efficient and accurate recognition data, therefore, it can be used for intelligent agriculture applications.
Irrigation for agriculture is the biggest consumer of freshwater in the world, which makes a case for the intensive use of technology to optimize the use of water, reduce the consumption of energy and improve the quality of crops. While the Internet of Things (IoT) and other associated technologies are the natural choice for smart water management applications, their appropriateness is still to be proven in real settings with the deployment of on-site pilots. Also, IoT-based application development platforms should be generic enough to be adapted to different crops, climates, and countries. The SWAMP project develops IoT based methods and approaches for smart water management in precision irrigation domain and pilots them in Italy, Spain, and Brazil. In this paper, we present the SWAMP view, architecture, pilots and the scenario-based development process adopted in the project.
This paper presents an adaptive network mechanism for a smart farm system by using LoRaWAN and IEEE 802.11ac protocols. Generally, the internet of things (IoT) system for agriculture application is used in an environment where significant interferences can occur. These interferences can disrupt the network performance of the system. In this paper, an adaptive network mechanism is designed to improve the network performance of the system, in order to achieve a more reliable smart farm system. Specifically, the proposed adaptive network mechanism is implemented in the application layer. The system has the ability to adjust a protocol based on the network condition. For instance, the IEEE 802.11ac is suitable for transmitting data which require high data rate such as image or video. On contrary, the LoRaWAN protocol is suitable for sending data that only have small data packets such as sensor reading data. An adaptive mechanism, which combines advantages of both protocols, leads the system to achieve reliability while performing the monitoring task. The system has been evaluated for the real deployment scenario. The result demonstrates that the proposed system brings the reliability in terms of average latency and total gathered number of sensors' data.
Proactive process adaptation can prevent and mitigate upcoming problems during process execution. Proactive adaptation decisions are based on predictions about how an ongoing process instance will unfold up to its completion. On the one hand, these predictions must have high accuracy, as, for instance, false negative predictions mean that necessary adaptations are missed. On the other hand, these predictions should be produced early during process execution, as this leaves more time for adaptations, which typically have non-negligible latencies. However, there is an important tradeoff between prediction accuracy and earliness. Later predictions typically have a higher accuracy, because more information about the ongoing process instance is available. To address this tradeoff, we use an ensemble of deep learning models that can produce predictions at arbitrary points during process execution and that provides reliability estimates for each prediction. We use these reliability estimates to dynamically determine the earliest prediction with sufficient accuracy, which is used as basis for proactive adaptation. Experimental results indicate that our dynamic approach may offer cost savings of 27% on average when compared to using a static prediction point.
Smart agriculture systems based on Internet of Things are the most promising to increase food production and reduce the consumption of resources like fresh water. In this study, we present a smart agriculture IoT system based on deep reinforcement learning which includes four layers, namely agricultural data collection layer, edge computing layer, agricultural data transmission layer, and cloud computing layer. The presented system integrates some advanced information techniques, especially artificial intelligence and cloud computing, with agricultural production to increase food production. Specially, the most advanced artificial intelligence model, deep reinforcement learning is combined in the cloud layer to make immediate smart decisions such as determining the amount of water needed to be irrigated for improving crop growth environment. We present several representative deep reinforcement learning models with their broad applications. Finally, we talk about the open challenges and the potential applications of deep reinforcement learning in smart agriculture IoT systems.
An ultimate guide to transfer learning in nlp
- P Bhavsar
Yolov4: optimal speed and accuracy of object detection
- A Bochkovskiy
- C Y Wang
- H Y M Liao
Map (mean average precision) for object detection
- J Hui
Smart water management platform: Iot-based precision irrigation for agriculture
- C Kamienski
- J P Soininen
- M Taumberger
- R Dantas
- A Toscano
- T Salmon
- R Cinotti
- A Torre Maia
- Neto